Multi-channel EEG signals classification via CNN and multi-head self-attention on evidence theory
Electroencephalography (EEG) provides valuable physiological information to identify
human activities. However, it can be difficult to analyze the EEG data in human patterns …
human activities. However, it can be difficult to analyze the EEG data in human patterns …
Predicting human intention-behavior through EEG signal analysis using multi-scale CNN
C Huang, Y Xiao, G Xu - IEEE/ACM Transactions on …, 2020 - ieeexplore.ieee.org
At present, the application of Electroencephalogram (EEG) signal classification to human
intention-behavior prediction has become a hot topic in the brain computer interface (BCI) …
intention-behavior prediction has become a hot topic in the brain computer interface (BCI) …
A coincidence-filtering-based approach for CNNs in EEG-based recognition
Electroencephalogram (EEG), obtained by wearable devices, can realize effective human
health monitoring. Traditional methods based on artificially designed features have …
health monitoring. Traditional methods based on artificially designed features have …
A convolutional recurrent attention model for subject-independent EEG signal analysis
The electroencephalogram (EEG) signal is a medium to realize a brain-computer interface
(BCI) system due to its zero clinical risk and portable acquisition devices. Current EEG …
(BCI) system due to its zero clinical risk and portable acquisition devices. Current EEG …
Deep Gaussian mixture-hidden Markov model for classification of EEG signals
Electroencephalography (EEG) signals are complex dynamic phenomena that exhibit
nonlinear and nonstationary behaviors. These characteristics tend to undermine the …
nonlinear and nonstationary behaviors. These characteristics tend to undermine the …
Universal joint feature extraction for P300 EEG classification using multi-task autoencoder
The process of recording electroencephalography (EEG) signals is onerous and requires
massive storage to store signals at an applicable frequency rate. In this paper, we propose …
massive storage to store signals at an applicable frequency rate. In this paper, we propose …
An explainable and generalizable recurrent neural network approach for differentiating human brain states on EEG dataset
Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI)
approaches. Despite the success of existing EEG approaches in brain state recognition …
approaches. Despite the success of existing EEG approaches in brain state recognition …
A comprehensive machine-learning-based software pipeline to classify EEG signals: A case study on PNES vs. control subjects
The diagnosis of psychogenic nonepileptic seizures (PNES) by means of
electroencephalography (EEG) is not a trivial task during clinical practice for neurologists …
electroencephalography (EEG) is not a trivial task during clinical practice for neurologists …
Complex networks and deep learning for EEG signal analysis
Electroencephalogram (EEG) signals acquired from brain can provide an effective
representation of the human's physiological and pathological states. Up to now, much work …
representation of the human's physiological and pathological states. Up to now, much work …
A Deep Learning‐Based Classification Method for Different Frequency EEG Data
T Wen, Y Du, T Pan, C Huang… - … Mathematical Methods in …, 2021 - Wiley Online Library
In recent years, the research on electroencephalography (EEG) has focused on the feature
extraction of EEG signals. The development of convenient and simple EEG acquisition …
extraction of EEG signals. The development of convenient and simple EEG acquisition …